Explores optimal conditions for hydrogen-rich syngas production from corn stover, suggesting efficient predictive modeling approaches.
High Resolution Image Download MS PowerPoint Slide Supercritical water gasification (SCWG) of biomass to produce hydrogen-rich syngas is a promising technology. However, the high-temperature and high-pressure conditions required for SCWG render the experimental evaluation of gas production performance both time-consuming and costly. Therefore, accurate predictive modeling is essential for determining the optimal process conditions and parameters. In this study, the effects of three process parameters (temperature, biomass concentration, and reaction time) on six performance indicators (gas yield (GY), carbon gasification efficiency (CGE), hydrogen selectivity (HS), methane selectivity (MS), carbon dioxide selectivity (CDS), and solid yield (SY)) were investigated using orthogonal experiments combined with an analysis of variance (ANOVA). A three-factor, three-level Box-Behnken design (BBD) was employed. The results indicated that temperature had a highly significant effect on all six responses, followed by biomass concentration, whereas the reaction time had a negligible effect, with the reaction nearly reaching completion within 15 min. The activation energy for the SCWG of corn stover in the range of 673–873 K was determined to be 88.73 kJ/mol. Predictive models for each response were established, all exhibiting high accuracy with a maximum deviation of only 3.11% between predicted and experimental values.
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Zhang et al. (2026) studied this question.
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